Dataset Information

An Immunity-Associated lncRNA Signature for Predicting Prognosis in Gastric Adenocarcinoma.

ABSTRACT:

Background

Gastric adenocarcinoma (GAD) is one of the most common tumors in the world and the prognosis is still very poor.

Objective

We sought to identify reliable prognostic biomarkers for the progression of GAD and the sensitivity to drug therapy.

Method

The RNA sequencing data of GAD was downloaded from the Cancer Genome Atlas (TCGA) database and used for analysis. Differentially expressed, immune-related lncRNA (DEIRlncRNA) was characterized by differential analysis and correlation analysis. Univariate Cox regression analysis was used to identify DEIRlncRNA associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to determine a signature composed of eight IRlncRNAs. Based on this signature, we further performed gene set enrichment analysis (GSEA) and somatic mutation analysis to evaluate the ability of this signature to predict prognosis.

Results

In total, 72 immune-related lncRNAs (DEIRlncRNAs) with prognostic value were identified. These lncRNAs were used to construct a model containing eight immune-related lncRNAs (8-IRlncRNAs). Based on this risk model, we divided GAD patients into high-risk and low-risk groups. The analysis showed that the prognosis of the two groups was different and that the high-risk group had worse overall survival (OS). Immune cell infiltration analysis showed that the proportion of memory B cells increased in the high-risk group while the proportion of macrophages M1, T cells, CD4 memory-activated cells, and T cell follicular helpers decreased. GSEA results showed that 8-IRlncRNA was significantly enriched in tumorigenesis pathways such as myc. The results of somatic mutation analysis showed that the CDH1 gene was significantly mutated in the high-risk group.

Conclusion

A prognostic signature of 8-IRlncRNAs in GAD was established and this signature was able to predict the prognosis of GAD patients.

SUBMITTER: Zhao X

PROVIDER: S-EPMC9061059 | biostudies-literature |

REPOSITORIES: biostudies-literature

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